Strategic Decision Support in Disaster Relief Logistics: Stochastic Network Optimization for Efficient Resource Location and Allocation

In response to the complex challenges of disaster relief logistics, we present a stochastic network modeling approach to develop insights into strategic facility location, capacity planning, and resource allocation of relief supplies. The primary purpose of the proposed model and numerical experiments is to present a cost-effective logistics network designed to efficiently handle multiple relief items across a spectrum of different crisis scenarios. By incorporating stochastic elements into the analysis, we aim to capture the inherent unpredictability of demand fluctuations and supply chain disruptions. Moreover, our approach centers on optimizing facility sizes to harness economies of scale and refining allocation decisions. To demonstrate the practical applicability of our model, we conduct a computational case study utilizing instances from the German national food stockpiling system. Additionally, we present a sensitivity analysis highlighting the impact of network modifications, crisis intensity, and scale effects on facility location and assignment decisions. The results provide managerial insights and economic considerations for decision-makers, enhancing the resilience and adaptability of cost-effective disaster preparedness and network design strategies.